phymath999: a trend mode (Diffusion Equation)

http://www.diwanyfi.com/Resources/Finance%20&%20Accounting%20books%20&%20Papers/Trading%20and%20Technical%20Analysis/Trend%20Trading%20Articles.pdf

Stocks & Commodities V. 20:1 (22-25): Detecting Trend Direction And Strength by Barbara Star, Ph.D.

PETER NEUMANN

T

TRADING BASICS

Combine ADX And MACD

Detecting Trend Direction

And Strength

Using an indicator by itself can reveal a portion

of the entire picture. Combining it with another

can reveal more.

by Barbara Star, Ph.D.

raders use technical indicators to

recognize market changes. They

look to indicators for signs of

price direction, momentum shifts,

and market volatility. Among the

most sought-after indicators are

those that identify price trends. Traditionally,

moving averages serve that purpose, but they

suffer from whipsaw action during price

consolidations. However, there is another

approach. This article shows how to combine two

popular indicators to help traders detect not only

trend direction but also trend strength.

The indicators involved are the average

directional index (A

DX) and the moving average

convergence/divergence (M

ACD). The ADX

functions as a trend detector, rising as price

strengthens into an identifiable trend and falling

when price moves sideways or loses its trending

power. A

DXvalues in the 20 to 30 range indicate

mild to moderate trending behavior, while

values above 30 usually signify a strong trend.

Unfortunately, the A

DX does not reveal the

trend direction. The M

ACD, on the other hand,

indicates price momentum and can also be used

to identify price direction as it rises above its

trigger line or falls below its zero line.

When both indicators are plotted on the

same chart, trend strength and trend direction

become clear. The chart of A

OL Time Warner

OL) in Figure 1 illustrates how the two

indicators complement each other. The A

in the upper panel rose from April through

May 2001, indicating a trending market. The

ACD rose above its dotted trigger line and its

zero line, showing that price direction was up.

During July and August the A

DX rose once

again, but the M

ACD was then below its trigger

Stocks & Commodities V. 20:1 (22-25): Detecting Trend Direction And Strength by Barbara Star, Ph.D.

line and its zero line, showing that a downtrend

was in progress.

T

HE CONFIRMING PATTERN

Most traders prefer the long side of the market

and look for an uptrending market. The

confirming pattern identifies exactly that

condition. When the A

DX and MACD move

up in unison, they confirm rising price

direction; the Bristol-Myers Squibb Co.

MY) chart in Figure 2 offers a good example

of a confirming pattern. The A

DX and MACD

rose as price moved up strongly in September

to December 2000.

When price changed direction in January

2001, both the A

DX and MACD followed suit.

The falling A

DX was not indicating that a

downtrend had begun; merely that it no longer

could find a trend. In this example, the M

ACD

showed that price was retracing its prior upward

march. But sometimes when both indicators

fall, price forms a sideways trading range, rather

than the more pronounced downward move

seen in this chart.

T

HE DIVERGING PATTERN

The indicator combination shines when a price

downtrend is in progress and they form a

divergence. The A

DX rises as it identifies the

trend, while the M

ACD falls below its trigger

line and often below its zero line. The two

indicators no longer move in tandem; instead,

they diverge and form almost a mirror image of

each other. During the severe 2000–01 decline

in Cisco Systems (C

SCO), the ADX-MACD

combination formed several easily identifiable

diverging patterns as one rose and the other fell

(Figure 3). They reflected the falling prices in

September–October and December 2000 time

periods, as well as the continuing decline in

February–March 2001.

The diverging indicator pattern should warn

those who want to go bullish to stay out of a

stock. However, for those who wish to sell

stocks short or purchase put options, the

diverging pattern provides a visual gold mine.

But expect a price shift when the indicators stop

moving apart and begin to move toward each

other (as they did in April and May).

T

HE CONSOLIDATING PATTERN

Prices tend to consolidate periodically during

an uptrending move prior to continuing the

trend or changing direction. The indicators

highlight a price consolidation when the A

falls, while the M

ACD remains near or above its

FIGURE 1: ADX AND MACD WITH AOL TIME WARNER (AOL).

The rising ADX in the upper panel does

not differentiate between up- or downtrending price movements. Plotting the MACD just below the ADX

makes the trend direction much easier to spot.

FIGURE 2: A CONFIRMING PATTERN ON BRISTOL-MYERS SQUIBB (BMY).

Both the ADX and the

MACD signal a rising trend is in progress when they move up together with price.

FIGURE 3: A DIVERGING PATTERN ON CISCO SYSTEMS, INC. (CSCO).

The indicators highlight a

downtrend by diverging and forming a mirror-like image.

Rising ADX

Price Direction Up

Price Direction Down

Rising ADX

Divergence

Divergence

Strong Uptrend

METASTOCK (EQUIS INTERNATIONAL)

Stocks & Commodities V. 20:1 (22-25): Detecting Trend Direction And Strength by Barbara Star, Ph.D.

The combination can help

traders stay on the right

side of the market and

increase the probability of

successful trading results.

zero line. This pattern often occurs following a

confirming pattern, as the chart of Bank of

America Corp. (B

AC) in Figure 4 illustrates.

Both indicators rose during the price uptrend

in December 2000 and January 2001. Both

indicators fell as price declined in February

2001. But the A

DX continued to decline, while

ACD remained at or above its zero line as price

entered a trading range consolidation in March

and April. Once prices resumed their upmove in

May, both indicators once again began to rise.

S

OME

OBSERVATIONS

• A

DX:

The ADX can be

confusing because it is

interpreted differently

from other indicators.

Most indicators move up

when prices rise, and

they fall when prices

decline. As seen in the

chart of Toys “R” Us (T

OY) (Figure 5), that was

not necessarily the case with the A

DX.

At point A the A

DX was rising while price

moved down. The A

DX pulled back slightly at

point B as prices rose. However, at point C the

DX rose in conjunction with prices. The ADX

declined between points C and D, while price

moved sideways before resuming the uptrend

indicated by point D. The A

DX dip into point E

paralleled a price decline during June. But instead

of a continuation of the preceding uptrend, the

next A

DX rise at point F was met with a further

decline in price. The moral? Don’t try to secondguess

price direction with the A

DX.

•

MACD: Even the venerable MACD misleads

us at times. Often, we forget the M

ACD is

basically a momentum indicator, so it does not

always accurately reflect price movement either.

Figure 6 displays an example with AT&T (T).

In addition to the A

DX and MACD in the upper

panels, I plotted a 13-unit simple moving average

of price on the chart. The 13-unit moving average

tends to correspond with the M

ACD solid line

crossing above and below its dotted trigger line

when the M

ACD is accurately tracking price.

FIGURE 4: A CONSOLIDATION PATTERN.

The box shows price consolidation that followed a price

uptrend in Bank of America (BAC) stock. The ADX declined but the MACD remained above zero to

reflect the consolidation.

FIGURE 5: ADX WITH TOYS “R” US (TOY).

By itself, the ADX can be confusing to interpret because

its ups and downs do not necessarily follow price.

FIGURE 6: MACD WITH AT&T (T).

Because it is a momentum indicator, the MACD does not always

track price accurately.

MACD at or above zero line

Consolidation

A B

C

D

E

F

13-unit moving average

2 3

1

Stocks & Commodities V. 20:1 (22-25): Detecting Trend Direction And Strength by Barbara Star, Ph.D.

At point 1, the M

ACD solid line rose above its trigger line,

which reflected the upmove in price. At point 2 the M

ACD

crossed below its dotted line, following price to the downside.

However, the M

ACD rise above its trigger line at point 3 was

not joined by rising prices or an upsloping moving average.

The M

ACD rose because downward momentum pressure had

diminished as prices slowed their downward descent.

•

Indicator combo: As the charts show, both the MACD and

the A

DX register their signals after the start of a price move,

with the A

DX slower to respond than the MACD. That means

the indicator combination will not pinpoint tops and bottoms.

However, traders can expect the A

DX–MACD combination to

identify and capture part of a trending move. More important,

it can help traders stay on the right side of the market and

increase the probability of successful trading results.

Barbara Star is a part-time trader and former university

professor. She is a past vice president of the Market Analysts

of Southern California and led a MetaStock users group for

many years. She is a frequent contributor to Technical

Analysis of S

TOCKS & COMMODITIES. Currently, she provides

individual instruction and consultation to those interested in

technical analysis.

S&C

Stocks & Commodities V. 18:4 (62-68): Picking Out Your Trading Trend by Martin J. Pring

CLASSIC TECHNIQUES

T

Pick Out Your

Trading Trend

There are three kinds of trends: short, intermediate, and long

term. This veteran trader and analyst explains how you can

spot them and use them.

by Martin J. Pring

echnical analysis assumes that all

the knowledge, hopes, and fears of

both active and inactive market

participants are reflected in one

thing: the price. Even if I am in a

cash position, I am still influencing

the price because it would be

higher if my cash were invested.

Thus, prices are determined by

Bull market

9-months -2 years

PRIMARY TREND

Approximately 4-years

Bear market

9-months -2 years

psychology. This would just be an interesting observation,

except that psychology moves in trends, and so do prices.

Most of the technical tools we use are aimed at identifying

trend reversals at an early stage. We ride on trends until the

weight of the evidence shows or proves that the trend has

reversed — in this case, the number of reliable technical

indicators all pointing in

the same direction.

Hence, the greater the

number of indicators signaling

a reversal, the

greater the probability

that a reversal will take

place. It is important to

remember that technical

analysis only deals in

probabilities, never certainties.

Unfortunately,

there is no known method

of forecasting the duration

and magnitude of a

trend with any degree of

consistency. Identifying

reversals is hard enough.

What

is a trend? How

long do they last? Before

the advent of intraday

charts, there were three

generally accepted durations

— primary, intermediate,

and short-term.

The main or

primary

trend (Figure 1) is often referred to as a

bull or bear market.

Bulls go up and bears go down. Typically, they last from

about nine months to two years, while the bear market

troughs are separated by just under four years. These trends

revolve around the business cycle and tend to repeat. This is

true whether the weak phase of the cycle is an actual recession

or there is no recession or growth.

A fourth category, the

secular trend, embraces several

primary trends and lasts between 10 and 25 years. An example

using US bond yields between the 1930s and the 1990s

can be seen in Figure 2.

Primary trends are not straight-line affairs, but consist of

a series of rallies and reactions. Those rallies and reactions

FIGURE 1: PRIMARY TREND.

The classic four-year trend is broken almost equally into

bull and bear modes.

FIGURE 2: SECULAR BOND TRENDS.

In 1982, the downtrend in bond prices broke along with inflation, setting off the greatest stock bull

market in history.

METASTOCK (EQUIS INTERNATIONAL)

Secular downtrend

Secular uptrend

US GOVERNMENT BOND PRICES

Stocks & Commodities V. 18:4 (62-68): Picking Out Your Trading Trend by Martin J. Pring

MARCI RASMUSSEN

are known as

intermediate trends and are represented in

Figure 3 by the solid blue line. They can vary in length from

as little as six weeks to as much as nine months — the length

of a very short primary trend. Intermediate trends typically

develop as a result of changing perceptions concerning economic,

financial, or political events.

It is important to have some understanding about the

direction of the main or primary trend. This is because rallies

in bull markets are strong and reactions weak, as shown in

Figure 3. On the other hand, bear market reactions are strong

while rallies are short, sharp, and generally unpredictable. If

you have a fix on the underlying primary trend, then you will

be better prepared for the nature of the intermediate rallies

and reactions that will unfold.

Classic technical theory holds that each bull market contains

three intermediate cycles, as does each primary bear

market (Figure 4). I would use this only as a guide, since

many primary trends are not easily classified this way. Thus,

if you are waiting for that third intermediate cycle in a bull

market, it may never materialize.

In turn, intermediate trends can be broken down into shortterm

trends that last from as little as two weeks to as much as

five or six weeks. They can be seen in Figure 5, represented

by the dashed red lines.

Stocks & Commodities V. 18:4 (62-68): Picking Out Your Trading Trend by Martin J. Pring

CALCULATING THE KST

The suggested parameters for short,

intermediate and long term can be

found in sidebar Figure 1. There

are three steps to calculating the

ST indicator. First, calculate the

four different rates of change. Recalling

the formula for rate of

change (R

OC) is today’s closing

price divided by the closing price

days ago. This result is then multiplied

by 100. Then subtract 100 to

obtain a rate of change index that

uses zero as the center point. Second,

smooth each R

OC with either a

simple or exponential moving av-

Short-term (D) 10 10 1 15 10 2 20 10 3 30 15 4

Short-term (W) 3 3E 1 4 4E 2 6 6E 3 10 8E 4

Intermediate-term (W) 10 10 1 13 13 2 15 15 3 20 20 4

Intermediate-term (W) 10 10E 1 13 13E 2 15 15E 3 20 20E 4

Long-term (M) 9 6 1 12 6 2 18 6 3 24 9 4

Long-term (W) 39 26E 1 52 26E 2 78 26E 3 104 39E 4

t is possible to program all KST formulas into MetaStock and the CompuTrac SNAP module.

(D) Based on daily data. (W) Based on weekly data. (M) Based on monthly data. (E) EMA.

where:

E2 = New exponential average

E1 = Prior exponential average

P2 = Current price

Please note the first day’s calculation does not have a prior

exponential average. Consequently, you just use the first

day’s price and begin the smoothing process the next day.

Figure 2 is a spreadsheet example of the short-term weekly

ST using exponential moving averages for the smoothing.

Column C is the three-week rate of change. The formula for

cell C20 is:

erage (E

MA). Third, multiply each smoothed ROC by its

prospective weight and sum the weighted smoothed R

OCs.

The formula for an exponential moving average (E

MA)

requires the use of a smoothing constant (

α) alpha. The

constant used to smooth the data is found using the formula

2/(

n+1). For example, for n=3, then α = 2/(3+1)=0.50. The

formula for the E

MA is:

E2 = E1 +

α (P2 - E1)

Cell G20 is a six-week R

OC:

=((B20/B15)*100)-100

Cell H20 is a six-week E

MA:

=H19+0.29*(G20-H19)

Cell I20 is a 10-week R

OC:

=((B20/B11)*100)-100

Cell J20 is an eight-week E

MA:

=J19+0.22*(I20-J19)

Finally, cell K20 is the summed weighted smoothed R

OCs.

Each smoothed R

OC is weighted according to sidebar

Figure 1 and summed:

=D20+(2*F20)+(3*H20)+(4*J20)

—Editor

SIDEBAR FIGURE 1:

The ROC column is the rate of change. The MA column is the moving average value,

and E after the moving average value indicates that the moving average is an exponential moving average.

Multiply each smoothed ROC by its weight prior to summing the four smoothed ROCs.

=((B20/B18)*100)-100

The three-week rate of change is smoothed with a

three-week E

MA. The constant used to smooth the

data is found using the formula 2/(

n+1). For n=3,

then, the constant equals 2/(3+1)=0.50, and thus, the

formula for cell D20 is:

=D19+0.5*(C20-D19)

Cell E20 is a four-week R

OC:

=((B20/B17)*100)-100

Cell F20 is a four-week E

MA:

=F19+0.4*(E20-F19)

123456789

1 0

1 1

1 2

1 3

1 4

1 5

1 6

1 7

1 8

1 9

2 0

A B C D E F G H I J K

Date S&P 500 3 week 3 Week 4 Week 4 week 6 Week 6 week 10 Week 8 week Summed

920103 419.34 ROC EMA ROC EMA ROC EMA ROC EMA Weighted

920110 415.10 ROC

920117 418.86 -0.11

920124 415.48 0.09 -0.92

920131 408.78 -2.41 -2.41 -1.52

920207 411.09 -1.06 -1.73 -1.86 -1.97

920214 412.48 0.91 -0.41 -0.72 -0.72 -0.63

920221 411.46 0.09 -0.16 0.66 -0.17 -1.77

920228 412.70 0.05 -0.05 0.39 0.05 -0.67

920306 404.44 -1.71 -0.88 -1.95 -0.75 -1.06 -3.55

920313 405.84 -1.66 -1.27 -1.37 -0.99 -1.28 -1.28 -2.23

920320 411.30 1.70 0.21 -0.34 -0.73 -0.29 -0.99 -1.80

920327 403.50 -0.58 -0.18 -0.23 -0.53 -1.93 -1.26 -2.88

920403 401.55 -2.37 -1.28 -1.06 -0.74 -2.70 -1.68 -1.77

920410 404.29 0.20 -0.54 -1.70 -1.13 -0.04 -1.20 -1.65

920416 416.05 3.61 1.54 3.11 0.57 2.52 -0.13 0.87 0.87

920424 409.02 1.17 1.35 1.86 1.08 -0.55 -0.25 -0.59 0.54

920501 412.53 -0.85 0.25 2.04 1.47 2.24 0.47 -0.04 0.42 6.26

920508 416.05 1.72 0.99 0.00 0.88 3.61 1.38 2.87 0.96 10.71

SIDEBAR FIGURE 2: SPREADSHEET FOR SHORT-TERM WEEKLY KST.

Here, the KST is calculated using exponential moving averages.

Courtesy Microsoft Excel

Stocks & Commodities V. 18:4 (62-68): Picking Out Your Trading Trend by Martin J. Pring

INTERMEDIATE TREND

Reactions

are strong

Rallies

are short

Corrections

are mild

Rallies

are strong

INTEGRATION OF PRIMARY AND INTERMEDIATE TRENDS

1

1

2

2

3

3

Classic bull market

has 3 intermediate

cycles

Classic bear market

has 3 intermediate

cycles

FIGURE 3: INTERMEDIATE TREND.

Pulsating in the midst of primary trends are shorter,

intermediate trends, giving charts a stairstep appearance.

FIGURE 4: THREE INTERMEDIATE CYCLES.

An idealized market cycle would have

three waves up and three waves down.

MARKET CYCLE MODEL

Short-term

trend

FIGURE 5: MARKET CYCLE MODEL.

Inside the intermediate cycles are short-term cycles

that last from two to six weeks.

T

HE MARKET CYCLE MODEL

Now that all three trends have been discussed, a

couple of points are worth making. First, as an investor,

it is best to accumulate when the primary trend is

in the early stages of reversing from down to up and

liquidating when the trend is reversing in the opposite

direction (Figure 6).

Second, as traders, we are better off if we position

ourselves from the long side in a bull market, since

that is the time when short-term trends tend to have the

greatest magnitude. By the same token, it does not

usually pay to short in a bull market because declines

can be quite brief and reversals to the upside unexpectedly

sharp. If you are going to make a mistake, it is

more likely to come from a countercyclical trade

(Figure 7). This is where the market cycle model

comes into play.

U

SING THE MARKET CYCLE MODEL

How can you put this into practice? My favorite

method is to plot three smoothed momentum indicators

to mimic the three trends. An example can be seen

in Figure 8 using the K

ST indicator, originally introduced

in S

TOCKS & COMMODITIES in the early 1990s.

The formulas for the three trends can be seen in the

sidebar, “The K

ST.”

It’s also possible to substitute other smoothed momentum

indicators. For example, three suggested

sets of parameters are displayed in Figure 9 for the

stochastic indicator. This arrangement is far from

perfect, but it does provide a framework that offers

the trader and investor a road map of the current

convergence of the short-, intermediate-, and longterm

trends. As always, it is important to ensure that

other indicators in the technical toolbox also support

this type of analysis.

This market cycle model approach can be applied to

intraday analysis. Obviously, the time frames will differ

radically from the primary, intermediate, and shortterm

varieties we looked at previously, but the principle

still applies. If you know that a powerful three- to fourday

rally is under way, it would be madness to short a

four-hour countercyclical move. Clearly, trading from

the long side would be more appropriate, but you would

only know this if you had identified the bullish intraday

primary trend in the first place. I will cover these

shorter-term aspects in another article.

I

N SUMMARY

There are three generally accepted trends: short-,

intermediate-, and long-term or primary. Secular, or

very long-term, trends also make up several primary

trends and can last between 10 and 25 years. At the

other end of the spectrum, intraday data now provides

us with trends of even shorter time spans lasting as

little as 10 to 15 minutes.

Stocks & Commodities V. 18:4 (62-68): Picking Out Your Trading Trend by Martin J. Pring

FIGURE 8: KST.

This indicator, developed by Pring in the early 1990s, is generally reliable in picking out trends.

Moody’s AAA bond yield

Short-term

KST

Intermediate

KST

Long-term KST

PRIMARY TRENDS

MOODY’S AAA BOND YIELDS AND THREE KSTs

It is important for investors to have some idea of the

direction and maturity of the main trend. Working on the

assumption that a rising tide lifts all boats, traders should also

try to understand the direction of the main trend even though

they themselves are only concerned with a short time horizon.

A convenient way to chart longer-term trends is to use a

smoothed momentum indicator such as the stochastics or K

ST.

Veteran trader and technician Martin J. Pring founded the

International Institute for Economic Research in 1981. Pring

is the author of several books, including the classic

Technical

Analysis Explained.

FIGURE 7: DON’T FIGHT THE TREND.

When trading in and out during a primary trend,

go in the direction of the primary trend, not against it.

MARKET CYCLE MODEL

Go long rallies

but do not

short reactions

Short reactions

but do not

go long rallies

MARKET CYCLE MODEL

Time to

accumulate

Time to

liquidate

FIGURE 6: ACCUMULATE/DISTRIBUTE.

Naturally, the best time to load up on stocks

is when a cycle bottom is at hand. Approaching the top, it’s time to distribute your holdings.

Stocks & Commodities V. 18:4 (62-68): Picking Out Your Trading Trend by Martin J. Pring

FIGURE 9: STOCHASTIC SMOOTHING.

Stochastics of differing-length parameters also pick up trends. You can smooth with any of a variety of

momentum indicators.

AAA yield

Stochastic (3x3x3)

Stochastic (10x10x6)

Stochastic (39x26x23) PRIMARY TRENDS

MOODY

’S AAA BOND YIELDS AND THREE STOCHASTICS

†See Traders’ Glossary for definition

S&C

R

ELATED READING

International Institute for Economic Research. Internet: http:

// www.pring.com/.

Pring, Martin J. [1992].

The All-Season Investor, John Wiley

& Sons.

_____ [1993].

Martin Pring On Market Momentum, International

Institute for Economic Research.

_____ [1985].

Technical Analysis Explained, McGraw-Hill

Book Co.

_____ [1992]. “Rate Of Change,”

Technical Analysis of

TOCKS & COMMODITIES, Volume 10: August.

_____ [2000]. “Trendline Basics,”

Technical Analysis of

TOCKS & COMMODITIES, Volume 18: March.

Stocks & Commodities V16:9 (425-427): Trading the Trend by Andrew Abraham

NEW TECHNIQUES

N

Trading

The Trend

Here’s a volatility indicator, presented here with simple

trend rules for trading various markets.

by Andrew Abraham

ew traders quickly become

familar with two adages: “The

trend is your friend,” and “Let

your profits run and cut your

losses.” Many of us, however,

have learned the hard way that

these things are easier said than

done. Why

is that? One reason

is lack of recognition, since the

trend itself is rarely clarified

and defined, let alone where it

starts and ends. So we need a clear explication of what a trend

is as well as where its beginning and its end are.

S

IMPLE ENOUGH

Simply, if the trend is considered up, then the trend of prices

are composed of upwaves and the downwaves are countertrend

movements. Downward trends are the opposite, seen as

downwaves with countertrend upwaves. Using several tools

and functions, we can design a quantifiable approach to

defining these waves. My favorite is the volatility indicator,

which is a formula that measures the market volatility by

plotting a smoothed average of the true range. The true range

indicator originates from the work of J. Welles Wilder Jr. from

his

New Concepts in Technical Trading Systems. The definition

of the true range is defined as the largest of the following:

• The difference between today’s high and today’s low

• The difference between today’s high and yesterday’s close,

• The difference between today’s low and yesterday’s close.

The calculation uses a 21-period weighted average of the true

range, giving higher weight to the true range of the most

recent bar. The final value is then multiplied by 3.

The volatility indicator is used as a stop-and-reverse method.

Let’s say the market has been rising, then the volatility

indicator is calculated each day and subtracted from the

highest close during the rising market. The highest close is

always used, even if there has been a series of lower closes

since the highest close. If the market closes below the

volatility indicator, then for the next day, the current reading

of the volatility indicator is added to the lowest close. This

step is followed each day until the market closes above the

trailing volatility indicator.

We now have a definition of the trend. An upward trend

exists as long as the volatility indicator is below the market

and a downtrend is in force if the volatility indicator is above

the market. To visualize these waves, we color-code the

uptrends blue and the downtrends red (Figures 1 and 2).

In addition, we can add a basic description of trends for

trading. We will say that uptrends are made up of waves of

higher highs, with prior lows not being surpassed. Conversely,

downtrends are composed of waves of lower lows

and prior highs not being surpassed. For sustained moves, the

upwaves during uptrends will be larger than the countertrend

downwaves, and in downtrends, the downwaves will be

larger than the countertrend upwaves. Therefore, we want to

only trade with the trend and buy upwaves in an uptrend and

sell short during a downtrend.

For example, as can seen in Figure 1, for Chase Manhattan

FIGURE 1: CHASE MANHATTAN BANK.

Use the volatility indicator to signal the

direction of the trend. Here, uptrends are in blue, and downtrends are in red.

FIGURE 2: CORN.

The trend is down during November, switches direction in

January, and returns down in March.

TRADESTATION (

OMEGA RESEARCH)

Stocks & Commodities V16:9 (425-427): Trading the Trend by Andrew Abraham

JOSÉ CRUZ

Bank, the upwave has higher highs

and the prior downwave was not surpassed,

so the market is in an uptrend;

look to buy only the upwaves. In

Figure 2, in the corn market, the opposite

situation exists and the same

concept is applied, except in this case,

the concept is in reverse because it is

a downtrend. During November, the

volatility indicator reversed trend, and

the prior low was broken. This was

our signal to go short. Our exit signal

will be the volatility indicator turning

positive.

The position was closed in January

1998, and since the rally’s high beginning

in January did not surpass the

highs of October, our second definition

of an uptrend was not met. As a result,

we went short again when the volatility

indicator went negative. In March, the

position was closed with a small loss,

and again, the highs of this upwave did

not surpass the highs of January, so we

had a signal to go short again when the

volatility indicator went negative and

the lows of February were broken.

T

HE TENETS OF

GOOD TRADING

Now we are developing the tenets of

good trading. We are trading with the

trend and locking in profits. But in

that case, how do we know the trend

might be ending?

As stated, an uptrend is intact until

the previous downwave in the uptrend

is surpassed. A downtrend is intact until

the previous upwave is surpassed. We

will use the lowest low while the volatility

indicator signals an uptrend for

our low point. This is just an alert that

possibly the trend might change. We

would still take the next trade in the

direction of trend (in a confirmed

uptrend, we take all upwaves, and in a

downtrend, all downwaves).

Our next step is to confirm whether

the trend has ended. This is confirmed

on our next wave. If we are in an

uptrend, and if our last downwave

went below the prior downwave, we

are on alert. If the next upwave surpasses

the prior upwave, our trend is

intact and our alert turned off.

In Figure 3, which shows a chart of

Stocks & Commodities V16:9 (425-427): Trading the Trend by Andrew Abraham

the Swiss franc, we went short in April 1997 and closed the

position in June 1997 with a nice profit. Because the highs of

the prior upwave were not surpassed, we know we are still in

a downtrend and went short again in June 1997. This trade did

not work, however, and the next blue upwave surpassed the

prior blue upwave; thus, we are on alert the trend might be

changing. We went short again in September 1997.

M

ULTIPLE TIME FRAMES

To enhance our performance in this strategy, we can use a

dual time frame. We look to a higher time frame to identify

the trend and only want to trade in that direction. In Figure 4,

we can see we are in a downtrend as well as a downwave on

the five-minute chart of the Standard & Poor’s 500 index, so

we only look to take trades to the short side on the one-minute

chart (Figure 5). We are short from approximately 11:30 in

the morning to the close. The trader looks to the lower time

frame to actually find the trades in the same direction of the

higher time frame.

On the one-minute chart, we are looking to trade only from

the short side because the five-minute bars are in a downtrend

from a little after noon. In our diagram, we see we had three

trades. Two of them worked and in the one that didn’t,

our loss was relatively small. If one-minute bars are too

short of a time frame, then consider trading five-minute

bars; the trader would look at the 15-minute chart to

determine the trend.

For example, if on the 15-minute chart he is in an uptrend

and identifies blue upwaves, he would go down to his fiveminute

chart, identify a red downwave and prepare a buy-stop

to pull him in the market if an upwave becomes present. The

same applies just in reverse for going short.

The time frames can be anything from a 10-tick or 25-tick

to a daily and a weekly. There must be substantial differences

between the two frames. Some ideas would be 15-minute

versus 60-minute, daily versus weekly, weekly versus monthly.

Neither we nor anyone else has developed a Holy Grail system

or an infallible trend indicator, but through diversification of

FIGURE 3: SWISS FRANC.

The downtrend from September to March was a smooth

decline.

FIGURE 4: S&P 500 FIVE-MINUTE BARS.

Midway through the trading day, the

trend was down.

FIGURE 5: S&P 500 ONE-MINUTE BARS.

There were two profitable short sell

signals, based on the trend of both the five-minute and one-minute bars.

noncorrelated markets and also a diversification of time frames,

the probability of success can be obtained.

S

UMMARY

Trading should be a simple application of a trend indicator,

such as the volatility indicator, and a trading plan with rules.

To enhance your profitability, consider using two different

time frames, one for the trend and a lower time frame to signal

your trades.

Andrew Abraham is a trader and a Commodity Trading

Advisor with Angus Jackson.

F

URTHER READING

Krausz, Robert [1996]. “Dynamic multiple time frames,”

Technical Analysis of

STOCKS & COMMODITIES, Volume

14: November.

Wilder, J. Welles [1978].

New Concepts in Technical Trading

Systems,

Trend Research.

†See Traders’ Glossary for definition

S&C

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

Rating Trend Strength

by Tushar S. Chande

Here's a simple indicator of trend strength. It goes like this: A value of +10 signals an uptrend; a value

of -10 signals a downtrend. S

TOCKS & COMMODITIES Contributing Editor Tushar Chande uses this simple

rating system to help answer the eternal traders' question: Is the market trending?

s you may have noticed, a number of rather complicated indicators are available to measure trend

strength. None of these indicators, unfortunately, is perfect. You could use J. Welles Wilder's average

directional index (A

DX) as an indicator of trend strength, or perhaps the r² value from linear regression

analysis. Or you could even use the vertical horizontal filter (V

HF) to help determine whether the market

is trending.

Each of these indicators requires the user to determine how many days' data should be used in the

calculations. As you vary the indicator length or number of days used in the calculation, however, the

result of the calculation changes also. Thus, there is no unambiguous answer. If the market were about to

enter or leave a trading range, you could get a different indication of trend strength every day — a

frustrating set of circumstances.

R

ATING THE TREND

Here is my way of rating a trend, a method I call

trendscore. If today's close is greater than or equal to the

x days ago, score one point. If today's close is less than the close x days ago, the trend's rating loses

one point.

Next, compare today's close to the close

x+1 days ago. If today's close is greater than or equal to that

close, score another point. Deduct one point if the close is lower than the prior close.

Article Text Copyright (c) Technical Analysis Inc. 1

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

If (today's close >= close

x days ago) then score = 1

If (today's close < close

x days ago) then score = -1

Add up the score for 10 comparisons; the score varies from + 10 to -10. If today's close is greater than all

the previous closes, then the trend's score is +10; if today's close is less than all the previous closes, the

score is -10. You could smooth? the data by adding fewer than 10 days or more than 10 days.

Trendscore = 10-day sum of scores from days 11 to 20

I begin my calculations at 11 days back from the present and go back another 10 days. Thus, I compare

today's close to the closes from 11 to 20 days ago. If today's close is greater than all 10 closes, then the

trend's score is +10. If today's close is less than the closes from 11 to 20 days ago, then the trend's score is

-10. In sideways markets, the score ranges from +10 to -10. A positive score shows an upward trend bias.

Similarly, a negative score shows a downward bias.

I prefer the 11- to 20-day period because it fits my trading horizon. A shorter time of comparison may be

too volatile, producing frequent trend change signals, while a longer comparison time is slow to respond.

During long trends, the trendscore remains at the outer limits, +10 or -10, for the duration of the trend. In

sideways markets, the score doesn't remain at +10 or -10 for long, oscillating between these limits.

Note how the V

HF indicates neither the sign nor the direction of

the trend, while the trendscore indicates both the trend direction

and trend strength.

M

ETASTOCK FORMULAS

We can use MetaStock to rate trends using the trendscore method . In MetaStock's formula builder, we

use the ref function to refer to past data:

TrendScore =

if(c,>=,ref(c,-11),1,-1)+if(c,>=,ref(c,-

12),1,-1)+if(c,>=,ref(c,-13),1,-

1)+if(c,>=,ref(c,-14),1,-

1)+if(c,>=,ref(c,-15),1,-

1)+if(c,>=,ref(c,-16),1,-

1)+if(c,>=,ref(c,-17),1,-

1)+if(c,>=,ref(c,-18),1,-

1)+if(c,>=,ref(c,-19),1,-

1)+if(c,>=,ref(c,-20),1,-1)

Figure 1 shows the trendscore for General Electric (G

E) common stock for 1987. Note how the score

vacillated during the sideways period from April to June. G

E's trendscore remained close to or at +10

from early June through mid-August, falling off close to the top. It rallied to +10 briefly in late

September and early October. However, it quickly settled to -10 well before the October 1987 crash. In

more recent price action, G

E'S score moved quickly but smoothly to catch the major trends (Figure 2).

The score was at +10 during each upward trend. The brief corrections were enough to send the score

Article Text Copyright (c) Technical Analysis Inc. 2

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

FIGURE 2: TRENDSCORE, GE, 1992-93.

In more recent price action, GE's score moved quickly but

smoothly to catch the major trends. The score was at +10 during each upward trend. The brief

corrections were enough to send the score down to -10 for short periods.

FIGURE 1: TRENDSCORE, GE, 1987.

Figure 1 shows the trendscore for General Electric (GE)

common stock for 1987. Note how the score vacillated during the sideways period from April to June.

E's trendscore remained close to or at +10 from early June through mid-August, falling off close to the

top. It rallied to +10 briefly, in late September and early October. However, it quickly settled to -10 well

before the October 1987 crash.

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

down to -10 for short periods.

Intel (I

NTC) had a big upward move in 1992-93 before entering a broad sideways period (Figure 3). The

trendscore was pinned to +10 during major portions of the upward move, and it was quick to change

directions during sideways periods. You can get a closer look at the trading range action in Figure 4. The

trendscore came off its +10 reading in late January 1993 and rallied back up to +10 in February through

March. However, it settled down in the -10 area on March 22. The -10 reading of April 15 caught the

break through 110 to the 90 area.

We would expect a loss in momentum as Intel enters the sideways range. You can verify this in Figure 5,

which displays the moving average convergence/divergence indicator (M

ACD). The MACD peaked in early

January and trended lower through April. Other long-range momentum indicators would confirm this

drop in momentum.

Figure 6 shows the 28-day vertical/horizontal filter. This trend indicator displays similar behavior in early

January, coming off its highs at almost the same time as the trendscore. V

HF formed a double bottom

between February and early April and has trended higher since. The trendscore flattened out at -10

somewhat before the V

HF. Note how the VHF indicates neither the sign nor the direction of the trend,

while the trendscore indicates both the trend direction and trend strength (+ 10 or -10).

A

MATTER OF STYLE

You could trade the trendscore many ways. You could use the zero crossing as an early signal. You

would then buy when the trendscore becomes positive and sell when it becomes negative. Or you could

wait one to three days after the trendscore reaches +10 or -10 before buying (+ 10) or selling (-10) . Or

you could combine the trendscore with a moving average, trading an upward or downward cross over.

Another variation would be to go long after the trendscore crosses from -10 to above +5 and go short

after the trendscore falls from +10 to below 5. The approach you choose depends on your trading style.

You could also smooth the trendscore with more or fewer days than I used in my calculations. You could,

for example, use fewer than 10 days for short-term and 20 to 30 days for intermediate-term trading. You

could also combine trendscore with other indicators of trend strength. For example, if you combined it

with the VHF indicator, trendscore would provide an indication of direction, while the V

HF could provide

additional information about the trend's strength.

You could also substitute intraday data in the trendscore method for short-term trading, using hourly data

to calculate a trend's score instead of daily data.

Trendscore is a simple way to rate trend strength. It indicates both the direction and strength of the trend

and can be easily combined with various trend-following strategies.

Tushar Chande, C

TA, holds a doctorate in engineering from the University of Illinois and a master's

degree in business administration from the University of Pittsburgh. He is a principal of Kroll, Chande,

& Co.

A

DDITIONAL READING

Appel, Gerald [1985].

The Moving Average Convergence-Divergence Trading Method , Advanced

References Copyright (c) Technical Analysis Inc. 3

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

FIGURE 3: TRENDSCORE, INTC, 1992-93.

Intel had a big upward move in 1992-93 before entering a

broad sideways period. The trendscore was pinned to +10 during major portions of the upward move,

and it was quick to change directions during sideways periods.

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

FIGURE 4: TRENDSCORE, INTC, EARLY 1993.

You can get a closer look at the trading range action.

The trendscore came off its +10 reading in late January 1993 and rallied back up to + 10 in February

through March. However, it settled down in the -10 area on March 22. The -10 reading of April 15

caught the break through 110 to the 90 area.

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

FIGURE 5: INTC, WITH MACD, EARLY 1993.

We would expect a loss in momentum as Intel enters

the sideways range. You can verify this here, where the moving average convergence/divergence

indicator(Macd) is displayed. The M

ACD peaked in early January and trended lower through April. Other

long-range momentum indicators would confirm this drop in momentum.

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

FIGURE 6: VHF WITH 28-DAY FILTER, EARLY 1993.

Figure 6 shows the 28-day vertical/horizontal

filter. This trend indicator displays similar behavior in early January coming off its highs at almost the

same time as the trendscore. V

HF formed a double bottom between February and early April and has

trended higher since. The trendscore flattened out at -10 somewhat before the V

HF. Note how the VHF

indicates neither the sign nor the direction of the trend, while the trendscore indicates both the trend

direction and trend strength (+10 or -10).

Stocks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Chande

Version, Scientific Investment Systems.

Colby, R.W., and T.A. Meyers [1988].

The Encyclopedia of Technical Market Indicators , Dow

Jones-Irwin.

Pring, Martin J. [ 1985].

Technical Analysis Explained , McGraw-Hill Book Co.

Wilder, J. Welles [1978].

New Concepts in Technical Trading Systems , Trend Research.

Stocks & Commodities V. 10:7 (313-315): Stocks According To Trend Tendency by Stuart Meibuhr

Stocks According To Trend Tendency

by Stuart Meibuhr

Many times, a question asked of S

TOCKS & COMMODITIES readers will more than likely find an answer —

and more than an answer, further questions. Such was the article that E. Michael Poulos presented early

in 1991, when he showed how assumed trend tendencies ain't necessarily so. Here, Stuart Meibuhr

answers one of those corollary questions. If certain futures contracts show decided trend tendencies, can

the same be said about certain stocks or indices?

he question that E. Michael Poulos asked in the January 1992 STOCKS & COMMODITIES was "Which

futures trend the most?" In turn, that question triggered a corollary question, "Which stocks or stock

indices trend the most?" Poulos's methodology involved measuring the difference between the highest

high and the lowest low for seven channel lengths (days) from 1 to 49. The range was averaged to arrive

at an average channel height for one-, two-, four-,nine-, 16-,25-, 36- and 49-day channels. Each average

was divided by the average for the one-day channel to arrive at a ratio.

Applying the same methodology to several market indices and seven stocks provided some enlightening

information. A spreadsheet program was used for the calculations on data transferred from a charting

program. Only those securities with histories dating to back before 1985 were used. Data for any holidays

were eliminated before the trend calculations. All calculations were performed on data dating from

January 2, 1985, to January 31, 1992, a period of seven years and one month.

S

IX SELECT

Article Text Copyright (c) Technical Analysis Inc. 1

Size of DB 1-d 4-d 9-d 16-d 25-d 36-d 49-d

Last year 1.00 2.10 3.20 4.42 5.75 6.77 7.55

Last two years 1.00 2.17 3.33 4.50 5.75 6.88 7.89

Middle one year 1.00 2.19 3.36 4.57 5.86 7.05 8.17

All 1.00 2.22 3.43 4.64 5.89 7.10 8.25

First year 1.00 2.23 3.42 4.59 5.74 6.94 7.96

First two years 1.00 2.23 3.42 4.65 5.87 7.05 8.03

RATIOS FOR THE OEX

For each security and index, six different time periods were analyzed.

FIGURE 1

Channel Square 7 yrs, 1 month from January 1, 1965

length root of

(days) length Channel height ratio to one

OTC SPX OEX MMI DJIA LLY NME IBM MER TX GM X

25-d 5 9.13 6.43 5.89 5.72 4.48 6.64 6.32 6.22 6.21 6.04 5.85 5.84

36-d 6 11.48 7.80 7.10 6.91 5.36 8.05 7.71 7.58 7.43 7.22 7.10 7.00

49-d 7 13.84 9.10 8.25 8.03 6.19 9.41 9.07 8.86 8.63 8.34 8.33 8.06

DATA FOR 7 YEARS AND A MONTH

An indication of trend tendency is if the ratio of the average channel height to the averge daily range is larger than the square

root of the channel length. The N

ASDAQ index showed the greatest tendency to trend, while Xerox ranked the least.

1-d 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

4-d 2 2.70 2.34 2.22 2.18 1.86 2.38 2.31 2.25 2.28 2.26 2.25 2.26

9-d 3 4.68 3.67 3.43 3.35 2.73 3.79 3.62 3.52 3.58 3.50 3.45 3.45

16-d 4 6.84 5.03 4.64 4.52 3.59 5.20 4.98 4.85 4.91 4.78 4.64 4.63

FIGURE 2

Stocks & Commodities V. 10:7 (313-315): Stocks According To Trend Tendency by Stuart Meibuhr

For each security, I analyzed six different time periods, which consisted of the entire data set; the first

year, the first two years; the last year; the last two years; and one year selected from the middle. This

ensured that the ratios were independent of the selected time periods. This turned out not to be

completely true. For example, the data in Figure 1 for the O

EX are shown for these six different time

periods.

Although some variations amounted to almost 10% between the smallest and the largest ratio for any

given time period, the trends from the shortest to the longest time period remained the same.

Consequently, the ratios for only the entire seven years and one month of data are reported here for the

other studied securities. These results for five stock market indices and seven stocks can be seen in

Figure 2.

The indices and the stocks are ranked separately in descending order of their ratios. The data for the S&P

500 represent only six years and seven months and differs significantly from those reported by Poulos.

The data here were for the S&P 500, whereas Poulos's data represented spliced future contracts and the

time periods covered were different. The trending tendency of indices appears to increase with the

increasing number of securities that make up that index. Unfortunately, that does not explain why the

Major Market Index (M

MI) (Figure 3) showed a greater trending tendency than did the Dow Jones

Industrial Average (D

JIA) (Figure 4), the tendency of which was extraordinarily low. The DJIA values

were consistently below the square root point, which, according to mathematician W. Feller, evinces a

lack of trends. All other indices showed strong trending characteristics, with the over-the-counter

ASDAQ) showing the strongest trending action (Figure 5).

All seven stocks showed good trending behavior, with Eli Lilly & Co. (L

LY) having the biggest numbers

and Xerox (X) ranking last for trending tendency. Other companies and symbols are: General Motors

M), IBM, Merrill Lynch (MER), National Medical Enterprises (NME) and Texaco (TX).

T

RADING IMPLICATIONS

If options are the tradeable, then it is imperative to follow the index on which the options are based and

not

the DJIA, because the DJIA tends not to trend. The same conclusion can be drawn about stocks; the

short-term trader would prefer to deal in options on stocks that have high trending behavior. Overall, with

this methodology, the trader can ascertain the trending behavior of any security before expending time

and capital on a trade.

Stuart Meibuhr trades stocks and options for his own account. He has lectured and taught on

computerized investment topics for the past 10 years.

A

DDITIONAL READING

Poulos, E. Michael [1992]. "Futures according to trend tendency, S

TOCKS & COMMODITIES, January.

Figures Copyright (c) Technical Analysis Inc. 2

FIGURE 3.

The Major Market Index when compared to the DJIA has a greater tendency to trend,

even though there are fewer stocks in the MMI.

FIGURE 4.

The DJIA showed less tendency to trend than the Major Market Index did.

FIGURE 5.

The NASDAQ index demonstrated the highest degree of trending tendency.

Stocks & Commodities V. 10:1 (38-42): Futures According To Trend Tendency by E. Michael Poulos

Futures According To Trend Tendency

by E. Michael Poulos

Not all markets have the same tendency to trend. E. Michael Poulos uses his February 1991 S

TOCK &

C

OMMODITIES article, "Of trends and random walks," on the random walk index, which separates trends

from random drifts by allowing for trend, as the basis of this article. He explains that the commodity

futures you may for one reason or another assume trend strongly may not in fact. By using similar

methods as previously, he produces a table of 28 commodities futures and debunks some futures

assumptions — for instance, there is a school of thought that assumes that crude oil, gasoline and

heating oil all show similar trending tendencies, whereas in truth crude oil and gasoline are near the top

of the list, and heating oil, the poor country cousin, comes out only near the middle. Poulos goes into

why.

hich futures trend strongest? My February 1991 article, "Of trends and random walks," explained

how the random walk index, which separates trends from random drifts by al- lowing for the direct

measurement of trend, could be used toward this end. (See sidebar, "The random walk index.") By using

a view of price-time history similar to the one used previously, we can determine how to rank various

futures according to their inclination of trend. We attempted to maintain objectivity by not requiring the

arbitrary choice of a predetermined fixed lookback interval (for example, the length of a moving

average). Other attempted rankings of this kind are often questionable in result because they do not

specially distinguish between random drifts and trends.

Some results may surprise you. For example, do you believe wheat trends stronger than corn? Or cattle

trends stronger than hogs? Wrong. Cattle and wheat are the weakest of the 28 futures covered here. Corn,

on the other hand, ranks near the top, sixth out of 28. Do you figure crude oil, gasoline and heating oil all

show the same tendency to trend? Wrongo! Crude oil and gasoline are near the top of the list, while

Article Text Copyright (c) Technical Analysis Inc. 1

Stocks & Commodities V. 10:1 (38-42): Futures According To Trend Tendency by E. Michael Poulos

heating oil is well down toward the middle.

Some explanations are in order. The average channel height for yen (Figure 1) provides some. For the

four-day channel length, for example, we start at Day 4 and look back for the highest high and the lowest

low from Day 1 through Day 4. We record that high to low difference. We then repeat the above for Day

2 through Day 5, 3 through 6 and so on. We then average all these heights to get the average channel

height figure for four day channels. This process is then repeated for each of the a various channel length

(that is, lookback intervals). The 2.29 ratio on the four-day row for yen is obtained by dividing the

average four-day channel height by the average one-day channel height (141.7 divided by 62.0). For the

sake of brevity, we show the average channel height only for yen, but the same procedure was used for all

28 futures (Figure 2).

As we indicated in "Of trends and random walks," these ratios follow, but tend to consistently exceed, the

square root of the number of days. Notice that wheat, the weakest trender, barely manages to get beyond

the square root figures (recall that 3 is the square root of 9, 4 is the square root of 16, and so forth).

Mathematician W. Feller showed that a "random walk" generated by tossing a coin (one step forward if

heads, one step backward if tails) would show a displacement from the starting point, depending on the

square root of the number of tosses.

The consistent move beyond the square root point seen in all markets is evidence of trends. The yen

clearly shows the strongest trending action, with its ratios well beyond the square roots, while wheat

shows much less evidence of trends.

"There are times, Loretta, when I wish I had remained a teacher at the Harvard business school."

The price data used for this study were spliced nearby futures contracts. The splicing is such that the data

file is always in the highest-volume nearby contract, with any price gap on rollover days shifted out by

adjusting the new contract. The historical period was January 1987 to June 1991, four and a half years.

The rankings of the British pound and wheat were two of the biggest surprises, as far as I was concerned,

so I thought it would be interesting to examine some of their charts. Figures 3 and 4 include the

Article Text Copyright (c) Technical Analysis Inc. 2

Stocks & Commodities V. 10:1 (38-42): Futures According To Trend Tendency by E. Michael Poulos

long-term random walk index (L

RWI), a trend indicator. An LRWI of highs greater than 1.0 indicates a

move beyond that expected for a random walk, and therefore an uptrend, while an L

RWI of lows greater

than 1.0 indicates a downtrend.

The charts show very clearly that the pound gets beyond the random walk boundary (with an index of

1.0) with greater strength and for more extended periods than wheat does.

If you're a trend-following futures trader, these rankings can help you answer one of your most important

questions of which future to trade.

E. Michael Poulos, (516) 423-2413, writes software and works in the research and development of

computer trading aids for Traders' Insight.

R

EFERENCES

Feller, W. [ 1968].

An Introduction to Probability Theory and Its Applications, Volume 1, John Wiley &

Sons.

Poulos, E. Michael [1991]. "Of trends and random walks," S

TOCKS & COMMODITIES, February.

Stewart, Ian [1989].

Game, Set and Math, Blackwell Publishing.

Weaver, W. [1982].

Lady Luck, Dover Publishing. Does not refer specifically to random walk theory but

does indicate that the standard deviation of the number of heads in a series of coin tosses varies as

the square root of the number of tosses.

FIGURE 1:

The ratio of each average channel height to the average channel height for one day was

greater than the square root of the length. This consistency indicates that the yen trends.

Figures Copyright (c) Technical Analysis Inc. 3

Stocks & Commodities V. 10:1 (38-42): Futures According To Trend Tendency by E. Michael Poulos

FIGURE 2:

The ratios shown above for yen and the S&P 500 are similar to those given in February,

even though the histories differ: January 1975 through April 1990 for yen, and June 1982 through April

1990 for the S&P 500.

Figures Copyright (c) Technical Analysis Inc. 4

Stocks & Commodities V. 10:1 (38-42): Futures According To Trend Tendency by E. Michael Poulos

FIGURE 3:

The pound ranked high for tending to trend. A reading greater than 1 for either the LRWI

highs or LRWI lows indicates a trend is under way.

Figures Copyright (c) Technical Analysis Inc. 5

Stocks & Commodities V. 10:1 (38-42): Futures According To Trend Tendency by E. Michael Poulos

FIGURE 4:

Wheat ranked last for trend tendency. Readings below 1 indicate a lack of a trend

Figures Copyright (c) Technical Analysis Inc. 6

Stocks & Commodities V. 10:1 (38-42): SIDEBAR: THE RANDOM WALK INDEX

THE RANDOM WALK INDEX

The channel height ratio to one day figures given show a consistent excess beyond the square root

column. This excess indicates the presence of trends and hints how to create a trend "yardstick." If no

trends were present, the ratios would be expected to all fall exactly on the square roots, and thus an

"expected random walk" over n days would be the square root of n multiplied by the average daily range

(same as average one-day channel height).

We define the random walk index (R

WI) as the ratio of an actual price move to the expected random

walk. If the move is larger than a random walk (and therefore a trend), its index would be larger than 1.0.

To keep track of where today's high is relative to previous lows and where today's low is relative to

previous highs, we need two indices:

WI of high=(H-Ln)/(Avg.mg.x n )

WI of low=(Hn-L)/(Avg.mg.x n )

where "Hn" and "Ln" are the high and lows of n days ago and "avg rng" is the average daily range over

the n days preceding today. In day-to-day use, these indices are calculated over a range of lookback

lengths. Use the largest value returned for today's indicator. Thus, we let the market determine the

lookback interval,rather than use a fixed arbitrary one as many current indicators do.

In addition, Figure 1 gives us a very important insight, showing the distribution of lookback lengths for

the largest R

WI (how many times did the largest RWI occur looking back two days, three days, four, five,

six...?). Since the curve of Figure 1 bends at a fairly sharp corner, the entire curve can be approximated

by only two straight lines. This means that the markets, to a very good approximation, can be thought of

as displaying two distinct personalities. The corner of Figure 1 is showing us where the dividing line

between short- and longterm behavior is, between seven and eight days. We therefore calculate two R

WIS,

one for short term (two to seven days' lookback), and one for longer-term (eight days and up). The

short-term one is a good overbought/oversold indicator and the long-term one is a very good trend

indicator.

Figures Copyright (c) Technical Analysis Inc. 1

Stocks & Commodities V. 10:1 (38-42): SIDEBAR: THE RANDOM WALK INDEX

SIDEBAR FIGURE 1

Figures Copyright (c) Technical Analysis Inc. 2

Stocks & Commodities V. 9:7 (298-300): What Is A Trend, Anyway? by John Sweeney

What Is A Trend, Anyway?

by John Sweeney

reader reacting to the Settlement article in January on trading basics (Settlement, "Trading simply:

Minimizing losses," Stocks & C

OMMODITIES, January 1991) asked a key question: What is a trend? How

do I identify it when I'm trading? (Personally, I use dual moving averages.) Most of us could think of a

number of ways of defining trends, but it fascinates me what our analytical methods tell us about our own

thinking. Typically, our thought of "trend" amounts to no more than drawing lines upward or downward.

I think it should also encompass drawing them horizontally.

The trend is our friend, we think, because that's when price changes occur in some unidentified, regular

progression upon which event we make money. Questioning the key elements — persistent movement

over time — by looking at charts, it's evident that tradeables can move smoothly, like the Eurodollars in

Figure 1 or Treasury bills (Fed managed), or abruptly, like, say, gold, which is notorious for opening $20

away from where it's been for the last six weeks (Figure 2).

My conclusion? Trending behavior varies by tradeable and is more apparent in "managed" prices (such as

specialist supported stocks and short-term interest rates) or markets of mammoth size where the sheer

number of participants precludes truly abrupt change — say, debt and currencies. Despite this, we look

for (hope for?) a straight line progression of prices from one level to another, hence the urge to draw

straight lines on

all charts of fluctuating prices.

By drawing straight lines, are we really trying to model this variegated behavior or just expressing our

own preconceptions and, thus, limitations of thought? Prices do exhibit persistence (that is, statistical

dependence): they usually open "around" where they closed and the next price is "around" where the last

price was. However, even if that phenomenon justifies describing that behavior with a single line, I can't

think of a reason for it to be straight. Though straight lines can be powerful analytical tools (Figure 3),

our strong preference for them is foremost our own wishful thinking.

S

TRAIGHT LINE ELEGANCE

A nifty refinement of the straight line approach was developed by John Ehlers for his cyclic analysis

work. He'd run a regression line (line A in Figure 4) through the last 20 days of data and record the R2.

Then he would extend the line one day further back in time and recalculate the R2. If it stayed the same

or increased, then the added day was consistent with the previous 20-day trend and he would go back

another day to repeat the calculation. Stepping backward in this fashion (line B in Figure 4), he'd

eventually find a point where the R2 decreased (the gap in Figure 4), an indication that the latest added

price was inconsistent with the most recent trend.

All these straight-line methods are explications of our intuitive

sense of expressing trend as direction, even if only horizontal.

The beauty of this approach is that it is indifferent to prices rising, falling or

staying level. It points out to

Article Text Copyright (c) Technical Analysis Inc. 1

Stocks & Commodities V. 9:7 (298-300): What Is A Trend, Anyway? by John Sweeney

FIGURE 1:

Short-term interest rates generally have good price continuity day to day, behavior

conducive to defining trend.

FIGURE 2:

While short rates have few gaps and small gaps, gold can move abruptly from one price

level to another, behavior different from an ideal trend.

(Data courtesy CompuTrac/M Dial Data)

Stocks & Commodities V. 9:7 (298-300): What Is A Trend, Anyway? by John Sweeney

FIGURE 3:

Straight lines are maligned as moronic but can be powerful tools, especially in detrended

series like the relative strength index. Here, RSI neatly calls the beginning and end of a trend in bonds.

FIGURE 4:

John Ehlers's elegant trend definition steps backward through the data until the coefficient

of determination decreases, indicating values inconsistent with the current trend.

Stocks & Commodities V. 9:7 (298-300): What Is A Trend, Anyway? by John Sweeney

FIGURE 5:

A 6% filter of the S&P 500 wipes out all the small moves, implicitly defining a number of

trends of that size or better.

FIGURE 6:

Averages are an intellectual extension of straight lines, but at least they reliably follow

prices without our intervention.

Stocks & Commodities V. 9:7 (298-300): What Is A Trend, Anyway? by John Sweeney

us that, quantitatively, trend can be persistent horizontal price levels (that is, lack of price movement) as

well as moving up and down. Since this view of price action is more comprehensive than the intuitive

"up or down," I prefer it. It subsumes the two-state model that prices are either moving or not moving and

suggests a quantitative approach to defining "movement," abnormal departures away from the regression

line or sharp changes in the slope and/or shortening of the period of the regression line.

Refinements, such as having a threshold level of change in the R2, using the slope of the regression line

to define breakout, or even checking beyond the point of declining R2 for prices that would return the

regression's coefficient to its previous values, are easy to imagine. It also ingeniously solves the time

issue: how long must a trend persist to be a trend? Ehlers's approach has its limitations, but it's robust and

rewards elaboration.

O

THER STRAIGHT-LINERS

In the vein of time, I also classify the various wave approaches (Elliott, Dow) as variants of the straight

line approach because they subsume price movement into lines between peaks and valleys. I've always

thought the best encapsulation of these approaches was Art Merrill's

Filtered Waves , Basic Theory,

which takes the straightforward approach that defining the percentage retracement would define the

waves — the trends — for you (Figure 5). Longtime S

TOCKS& COMMODITIES readers may be familiar

with these retracement charts from Art's monthly column, which usually uses them for comparison

against various indicators. Software for generating them is included in RTR's Technifilter Plus or

MetaStock Pro 2.5.

A 5% filter, for instance, would "filter out" all movements of less than 5% from the previous high or low.

Here, it's easy to see that one's "scale" — and, effectively, one's time horizon (given normal movements)

— can be set by the size of movement one seeks. I personally look for a 6% wave in the Standard &

Poor's, so I'm unlikely to see a minor wave (a "minor" trend) of 1% or 2%. I'm also trading in a different

realm than those following the Dow theory (10% to 25%).

B

EYOND STRAIGHT LINES

All these straight-line methods are explications of our intuitive sense of expressing trend as direction,

even if only horizontal. Once secure with line-drawing, averages are a refinement, since they reliably

follow prices (Figure 6) without our intervention or judgment. In trends, they aren't quite straight but are

close enough to make the connection for most people.

True refinements are French curves and fitted curves such as Bezier and regressions to exponential

curves (

à la Tom Kimball of Florida, of newsletter fame). Here, regression could also be used, perhaps

searching through an entire family of potential curves for consistent fit over a given period of time.

Again, price action is reduced to a simple line, but it's meant to be more indicative of the market's action.

To wrap up, we usually identify trend after it's started by the slope of the lines we draw on our charts,

whether straight or curved. However, if trend is persistence in price movement over time, trading range

activity is a trend of sorts. We'll need a sharper definition if we just want price activity that's going

somewhere. Our inability to isolate what we want is our own limitation: a predilection for simple lines.

John Sweeney is

STOCKS & COMMODITIES' Technical Editor.

Figures Copyright (c) Technical Analysis Inc. 2

Stocks & Commodities V. 8:10 (377-381): Early Trend Identification by John F. Ehlers

Early Trend Identification

by John F. Ehlers

mpressive profits can be accumulated just by staying with a position during a trend. We would all be

millionaires if only we could identify the trend early in its onset. While the trends are obvious in

retrospect, it's another matter altogether to identify the trend in the heat of battle. Not only that, there may

not be a trend at all at the time we expect one.

If we make a reasonable mathematical model of the market we can examine it parametrically. The

conclusions we draw from this model can help us establish our entry points and strategies for trading the

trends. We will view the market as a random walk problem to create our model.

Random walk for the market

In the same way that water can only flow downstream, time cannot be reversed in trading. In addition,

prices can only be higher or lower in the same way that the river can only bend to the right or left. These

elements constrain the random walk problem to a special form that mathematicians call "drunkard's

walk." In the simplest form of this walk, the "drunk" steps only into a square diagonally to the right or

into a square diagonally to the left as he steps forward. He must make a new decision with each step. To

make the decision random, he flips a coin to determine the direction he will take. Repeated many times,

the overlay of paths that he follows will look like a smoke plume. The question of the drunkard's

destination can be answered through a well-known partial differential equation called the Diffusion

Equation. The density of the smoke particles in the plume is analogous to the probability of the

drunkard's location. A multiple-exposure photograph of the drunkard's walk repeated over and over

would show its randomness. This photograph would show the composite paths to have a uniform density,

Article Text Copyright (c) Technical Analysis Inc. 1

Stocks & Commodities V. 8:10 (377-381): Early Trend Identification by John F. Ehlers

widening from the initial position. The uniform density would make the sum of the paths look like smoke

plume.

Further, random walk does not necessarily mean chaos. A minor variation of the drunkard's walk problem

is to allow the random coin-flip decision to control the change of direction rather than the direction

itself— that is, the random variable becomes momentum instead of direction. The partial differential

equation describing this condition is known as the Telegrapher's Equation. The equation describes

electric waves along telegraph wires, among other subjects. You can picture the result as the drunk

reeling back and forth. He overcorrects around a general direction trying to reach an objective. This

formulation of the problem, expressed in terms of physics, accurately portrays the river and explains why

the river meanders. In a multiple-exposure photograph the paths are still randomly distributed.

Nevertheless, the cycles are apparent in the shorter case of a single path. By analogy, the market has

short-term cycles when the appropriate conditions prevail.

If enough traders ask themselves whether the market will go up today, the random variable is direction.

Thus, conditions are established for the solution of the Diffusion Equation. On the other hand, if enough

traders ask themselves whether the trend will continue, the random variable now becomes momentum.

You could then expect the conditions to be established for the solution of the Telegrapher's Equation. The

market is ripe for short-term cycle activity.

Identifying trends with reverse logic

As formed by the random walk, our market model is either cyclic or trending. A moving average is about

the only means we have to measure the trend directly. Moving averages are not very helpful because they

are always lagging functions. However, we can measure the cycles and know when the market is cyclic.

By reverse logic, if the market is not short-term cyclic, it must be trending. We can identify whether the

market is cyclic in a period as short as a half cycle. Cycle analysis, therefore, can be used to spot a trend

early in its formulation.

The early identification of a trend then depends on a valid measurement of short-term cyclic activity.

There are two ways to do so, either by cycle elimination or by spectrum analysis. Of the two, cycle

elimination is by far the easier.

Let's approach the question of cycle elimination using synthesis and then reverse the procedure to

establish what we must do to perform the analysis. We can synthesize a theoretical price curve by adding

a pure sinewave to a straight trendline. We then examine these two components independently. The

average over the period of a theoretical sinewave is always zero, regardless of where we started the

average. If we used a moving average with a length the period of the sinewave, then the sinewave is

completely removed and we are left with only the straight line trend.

The identification of the trend is that easy. We eliminate the cyclic component when we use the average

over the cycle length. We could adjust the average as the cycle length varies and plot the results

day-by-day. I call the result an "instantaneous trendline." A fixed-length moving average can suffice

during periods when the cycle length is not changing. We expect the price to alternate across our

instantaneous trendline because the price has the cyclic component. We expect to see the crossing occur

approximately every half cycle. If the price fails to cross the instantaneous trendline, we get a clear signal

that the price has moved into a trend mode—that is, the movement in the direction of the trend swamps

the cyclic movement so the expected crossing does not occur. When this happens, the price parallels our

Article Text Copyright (c) Technical Analysis Inc. 2

FIGURE 1.

We can identify a trend in the first five days of its move on March 2, 1990. At this point

we have a 10-day cycle, and the price has not crossed the instantaneous trendline within the

last five days.

FIGURE 2.

This spectrum shows an excellent 12-day cycle in February 22, 1990, just after we

entered our short position.

FIGURE 3.

Here, the spectrum is taken on February 27, 1990. Note the subtle change. The

very long cycle is starting to appear.

FIGURE 4.

Figures 4, 5 and 6 show the progression of the spectrum for the next three trading

days.

FIGURE 5.

The progression of the spectrum continues.

FIGURE 6.

March 2, 1990, was the day previously declared that the trend was to be established.

Stocks & Commodities V. 8:10 (377-381): Early Trend Identification by John F. Ehlers

instantaneous trendline without crossing it. The instantaneous trendline is a lagging function like a

normal moving average. Using the instantaneous trendline method, a trend is identified when the price

does not cross or even appear likely to cross the trendline within a half cycle.

Figure 1 is an example of where we identify a trend in the first five days of its move on March 2, 1990

(900302, the cursor location). At this point we have a 10-day cycle, and the price has not crossed the

instantaneous trendline within the last five days. The price shows no tendency of trying to cross the

instantaneous trendline. Early identification allows us to capture about a 30-point profit, the majority of

the move.

We can use this technique to simply trade the trends. However, the profits are even better if we use the

trend identification to shift from a cyclic trading strategy to a trend trading strategy. Suppose in our

example we had been trading on the basis of cycles. Trading every five days (each half cycle), we would

have gone long on 900131, a short-term low. From there we would go short on 900207 (short-term high),

long on 900214 (a little early for a short-term low), and short on 900221. Our last short entry would be at

about 431, substantially above the 415 price where we first identified the downtrend. We would already

have been in a short position on the basis of cycle trading and therefore would exploit the full extent of

the trend movement. Shifting between cycle trading strategy and trend trading strategy therefore enhances

overall profitability.

Verifying trend identification

A spectrum display shows amplitude on the Y axis vs. cycle length on the X axis. This display allows

you to see the relative strength of several cycles, a benefit beyond merely picking out the dominant cycle.

The spectrum display also allows you to identify the quality, or resolution of the cycle measurement.

Ideally, a cycle measurement is a single spike on the display. This ideal picture tells you that there is only

one well-defined spectrum component — the dominant cycle. But what if the spectrum display is a broad

bell-shaped curve? In this case, the energy is spread over a range of possible dominant cycles, with no

cycle length being clearly dominant. The spectrum display indicates that the lack of resolution is reason

enough not to trade the market on the basis of cycles. For trend identification we are most interested in

the capability of the spectrum display to show the formation of two or more cycles.

Figures 4, 5 and 6 show the progression of the spectrum for the

next three trading days. Figure 6 is the spectrum for 900302, the

day we previously declared the trend to be established.

J.M. Hurst, in

The Profit Magic of Stock Transaction Timing, advances the principle of proportionality.

Simplified, the principle states that longer cycles have larger amplitudes. This principle is obvious to the

most casual chart reader.

We can use this principle to identify trends with the spectrum display of short-term cycles. From our

example for gold, Figure 2 shows an excellent 12-day cycle on 900222, just after we entered our short

position. Figure 3 shows the spectrum taken on 900227. The very long cycle, longer than 50 days, is

starting to appear. Figures 4,5 and 6 show the progression of the spectrum for the next three trading days.

Figure 6 is the spectrum for 900302, the day we previously declared the trend to be established. Figure 7

shows the spectrum three trading days later on 900307. Figure 7 shows that the short-term cycle has been

Article Text Copyright (c) Technical Analysis Inc. 3

FIGURE 7.

Three trading days later on March 7, 1990, the spectrum shows the short-term

cycle to be swamped by the trend, interpreted as a long cycle outside the calculation

range.

FIGURE 8.

The spectrum for April 18, 1990, still shows substantial long cycle energy,

however.

FIGURE 9.

The absence of long cycle energy for April 25, 1990, confirms the trend has

ended.

Stocks & Commodities V. 8:10 (377-381): Early Trend Identification by John F. Ehlers

swamped by the trend, which is interpreted as a long cycle outside the calculation range. Used this way,

the spectrum confirms that the trend has been established.

The spectrum can also confirm that the trend movement has ended. The price first crosses the

instantaneous trendline from the bottom on 900418. (We could have exited then at about 385 for a total

profit of $4,600 on a single contract.) Figure 8 is the spectrum for 900418, and shows long cycle energy.

Figure 9 is the spectrum for 900425, five trading days later. Absence of long cycle energy confirms the

trend has ended.

I'm trying to automate the entire trading strategy. One of the early

dreams for computers, you may recall, was to create robots to

serve mankind.

Helpful cycles and trading strategy

Our example is not an uncommon event. This approach can be used to repeatedly alter your trading

strategy as the market shifts from the cycle mode to the trend mode. All you need to do is estimate or

measure the current short-term cycle and then take a simple average over the period of the cycle length

and plot it as a point on your bar chart. Repeat this daily. Connecting the averages with a line creates your

"instantaneous trendline." Then watch the price action relative to this trendline to identify the onset of the

trend when the price has not crossed within the last half cycle.

I'm trying to automate the entire trading strategy. One of the early dreams for computers, you may recall,

was to create robots to serve mankind. By recognizing when we are in a trend mode (Diffusion Equation)

or cycle mode (Telegrapher's Equation), our computers should know when to apply the proper trading

strategy. I guess that would make our computer a "know-bot"!

John Ehlers, Box 1801, Goleta, CA 93116, (805) 969-6478, is an electrical engineer working in

electronic research and development and has been a private trader since 1978. He is a pioneer in

introducing maximum entropy spectrum analysis to technical trading through his MESA computer

program.

References

Hurst, J.M. [1970].

The Profit Magic of Stock Transaction Timing, Prentice-Hall.

Ehlers, John [1990]. "1989 cycles,"

Technical Analysis of Stocks & Commodities , June.

Figures Copyright (c) Technical Analysis Inc. 4

Stocks & Commodities V. 5:2 (64-66): Trend of the trend by Gregory L. Morris

Trend of the trend

by Gregory L. Morris

ost indicators of trend are taken for granted even though many times they are used successfully by

stock and commodity traders. It has been my experience that blindly following canned indicators can lead

you into a false sense of security, especially if you begin using the indicator when it is correctly calling

the market. If you begin using a trend-following indicator during its inevitable whipsaw period, you will

lose faith and look for another indicator. Therefore, if you develop an indicator using some basic logic

and reason which is related to known market action, you can have a little more faith in a particular

indicator. There is also the argument of using a basket of indicators and/or using them in a tree structured

approach. No doubt that is a safer approach, but it is not the purpose of this article.

It is accepted that the successful trader must identify and follow the trend of the market to be a consistent

winner. There are, of course, many indicators available to help identify the termination of a trend and

prepare you to reverse your positions. Adding even more confusion to the arena, you have to determine

which type of trend is being identified: short, medium, or long. Again, this is not the purpose here.

I would like to share with you a simple trend-following technique that seems to work very well. It works

because you must adapt it to the market you want to analyze. In other words, the parameters are going to

be different for each market, whether it be stocks, commodities, mutual funds, or whatever. A complete

explanation of the system will be discussed while being applied to the Dow Jones Industrial Average. I

know what you're thinking--no one can trade the DJIA, so why use it? That's the very reason I have used

it. I did not want it to look like I had culled hundreds of charts to find one that best supported this

technique.

First of all, you must determine your trading objectives: short, medium, or long-term. Short-term (a few

days to a few weeks) would rely on daily data for the trend information. Long-term (greater than six

months) would use almost exclusively weekly data. Medium-term would use a combination of both.

Then, of course, there are combinations of daily and weekly that you can use to put conditional restraints

into your trading system. The technique of using longer-term indicators to determine which side of a

shorter-term indicator to make your trade is usually a profitable trading strategy. However, for the

purposes of this article, I will stick to the short- to medium-term.

Determining the dominant short-term cycle is necessary to obtain the smoothing parameters for this

indicator. There are many good books available on cycles. One that I have found to be the most useful is

The Profit Magic of Stock Transaction Timing

title, the book is exceptionally logical in its explanation of market cycles and how to identify them.

One method of determining cycles is to detrend the data. This is a simple concept involving the price data

and a moving average. The moving average length is based upon the trend you want to follow. For

short-term, a moving average of 25-35 days works quite well. Basically, you subtract the moving average

from the price and plot the results. This is as if you had grasped the moving average line at both ends and

pulled it tight so it looked like a straight line with the price data remaining in its same relative position to

the moving average.

Article Text Copyright (c) Technical Analysis Inc. 1

Stocks & Commodities V. 5:2 (64-66): Trend of the trend by Gregory L. Morris

Most indicators of trend are taken for granted even though many

times they are used successfully

Of course you can always just count the days between lows from any daily chart or use sophisticated

maximum entropy or Fourier analysis. Detrending just makes those lows stand out a little better.

Before I go any further, a look at moving averages might be a good idea. A moving average smooths a

sequence of numbers such that the result is a reduction in magnitude of the short-term fluctuations, while

leaving the longer-term fluctuations little changed. Obviously, the time span of the moving average used

will alter its characteristics.

J.M. Hurst explains these alterations with three general rules:

1. A moving average of any given time span exactly reduces the magnitude of the fluctuations of duration

equal to that time span to zero.

2. The same moving average also greatly reduces (but does not eliminate) the magnitude of all

fluctuations of duration less than the time span of the moving average.

3. All fluctuations of greater than the time span of the average "come through," or are also present in the

resulting moving average line. Those with durations just a little greater than the span of the average

are greatly reduced in magnitude, but the effect lessens as periodicity duration increases. Very long

duration periodicities come through nearly unscathed.

For this indicator you need to identify the short-term cycle for the market you are analyzing. Detrending

the data as mentioned earlier will assist you in identifying market lows and finding the dominant

short-term cycle. Once the cycle has been identified, select an exponential average equal to one half of

the short-term cycle. For the Dow Jones Industrial Average, the short-term cycle is 14 to 15 days.

Therefore, you should use seven days for your exponential average. Most software programs allow you to

work with periods instead of smoothing constants when dealing with F exponential averages. Periods are

somewhat easier to grasp than smoothing constants. The reason behind using an average equal to one half

of the short-term cycle is to maximize the price movement without smoothing the dominant cycle.

Only through years of use and experimentation have I been able to determine the second part of the

equation: That is, the length (or period) of the second exponential average used with this trend-following

indicator. Simply stated, use a period six times the value of what you used for the short-term average. If

you used seven days for the short one, then use 42 days for this one. I suppose, for credibility, I should

have told you that by using six times the short average you were applying the principle of "half-dozening"

which, of course, everyone knows about. But, in case you don't,

half-dozening refers to the completely

arbitrary rule c of using a longer term average equal to six times the short average. This was found after

many years of experimentation.

The relationship between these two is similar to the Moving Average Convergence Divergence (MACD)

first written about by Gerald Appel. Merely subtract the longer period average from the short period

average and you are left with an oscillator that will give quicker and more timely signals than your

standard two-moving-average crossover system. Buy and sell signals are generated by using an arithmetic

moving average on this oscillator. Again, by much testing, I have found that the period for this average

should be three times the value of the short-term exponential average. In this example, that would be 21

days.

Article Text Copyright (c) Technical Analysis Inc. 2

Stocks & Commodities V. 5:2 (64-66): Trend of the trend by Gregory L. Morris

That's it: a simple trend-following indicator that works. Figure 1 shows the Dow Industrials and this

indicator over the last 14 months with the buy and sell signals identified. Note how the cursor will help

you identify actual crossovers by showing the value of the indicator and the value of the moving

averages. Figure 2 shows the same information but only for the last seven months.

Blindly following canned indicators can lead you into a false

sense of security

An additional technique to help avoid or reduce whipsaws is to construct a trading band around an

arithmetic moving average that uses the same period as the buy/sell average discussed above (21 days).

This trading band is on the price action itself and the percentage for the band is one that will encompass

most of the data or at least 90 to 95% of the data. The best buy and sell signals occur when the price

action is at or near the limits of the trading band. Obviously, buy signals should be accompanied by price

action at or near the lower band and sell signals at or near the upper band. If it is near the center or near

the opposite side, you ignore the signal given by the oscillator. If the price action is outside of the trading

band, the signal is probably premature. That's the nature of momentum and is another subject entirely.

Figure 3 shows the Dow Industrials with trading bands of 4.5 percent on the top plot and the indicator

with the new buy and sell indications at the bottom. Note that the indicator plot was changed to just a line

plot instead of the histogram plot as shown in Figure 1. Again, the last seven months are shown for better

detail (see Figure 4). Notice the reduction of signals when applying the trading bands to the system.

Remember, determine the dominant cycle. Select the short exponential average equal to one half of that

cycle. Use a longer term exponential average equal to six times the short one. Then place an arithmetic

moving average over the difference between the two exponential averages. The length of the arithmetic

average should be three times that of the short exponential average. Use trading bands to help filter out

some of the whipsaws. Whipsaws are a fact of life in a trend-following indicator--accept them and you

will always be on the right side of the market. If you start with a series of small losses, you will really get

excited when the big moves come.

I have prepared a small list of parameters that I have discovered to be the best (so far) when utilizing this

technique. The first number is the short exponential average, the second number is the long exponential

average, and the last number is the arithmetic moving average used on the oscillator and for the trading

bands.

Selected Parameters

Just to show you that this can work elsewhere, Figure 5 shows a sell signal just before an 11.88 point

drop in the S&P 500 in September 1986.

Article Text Copyright (c) Technical Analysis Inc. 3

Stocks & Commodities V. 5:2 (64-66): Trend of the trend by Gregory L. Morris

These are just a few examples of the parameters what I have found to be fairly reliable. As a stand-alone

indicator, this one works quite well. However, if used with a basket of indicators, overall results improve

significantly.

Gregory L. Morris is the president of G. Morris Corporation, which is engaged in technical analysis

consulting and software development.

Figure 1:

Figure 2:

Figures Copyright (c) Technical Analysis Inc. 4

Stocks & Commodities V. 5:2 (64-66): Trend of the trend by Gregory L. Morris

Figure 3:

Figure 4:

Figures Copyright (c) Technical Analysis Inc. 5

Stocks & Commodities V. 5:2 (64-66): Trend of the trend by Gregory L. Morris

Figure 5:

Figures Copyright (c) Technical Analysis Inc. 6

phymath999

Tuesday, August 28, 2012

a trend mode (Diffusion Equation)

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